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Hospital patient outcomes can be improved by the early identification of physiological deterioration. Automatic methods of detecting patient deterioration in vital-sign data typically attempt to identify deviations from assumed normal physiological conditions, which is a one-class approach to classification. This paper investigates the use of a two-class approach, in which abnormal physiology is modelled explicitly. The success of such a method relies on the accuracy of data labels provided by clinical experts, which may be incomplete (due to large dataset size) or imprecise (due to clinical labels covering intervals, rather than each data point within those intervals). We propose a novel method of refining clinical labels such that the two-class classification approach may be adopted for identifying patient deterioration. We demonstrate the effectiveness of the proposed methods using a large dataset acquired in a 24-bed hospital step-down unit.

Original publication




Journal article


Ieee trans inf technol biomed

Publication Date





1231 - 1238


Biomedical Engineering, Blood Pressure, Health Status, Heart Rate, Hospitalization, Humans, Medical Informatics, Models, Statistical, Oxygen, Respiratory Rate, Signal Processing, Computer-Assisted, Support Vector Machine, Vital Signs